Scientific Workflows in Heterogeneous Edge-Cloud Computing: A Data Placement Strategy Based on Reinforcement learning

TL;DR

This paper introduces a reinforcement learning-based data placement strategy for scientific workflows in heterogeneous edge-cloud environments, effectively reducing data transmission time by dynamically optimizing dataset distribution.

Contribution

It presents a novel two-stage data placement model combining particle swarm optimization and reinforcement learning for dynamic dataset distribution in edge-cloud workflows.

Findings

Reduces data transmission time compared to existing strategies

Effective dynamic dataset distribution across geographical regions

Improves workflow execution efficiency in heterogeneous environments

Abstract

The heterogeneous edge-cloud computing paradigm can provide an optimal solution to deploy scientific workflows compared to cloud computing or other traditional distributed computing environments. Owing to the different sizes of scientific datasets and the privacy issue concerning some of these datasets, it is essential to find a data placement strategy that can minimize data transmission time. Some state-of-the-art data placement strategies combine edge computing and cloud computing to distribute scientific datasets. However, the dynamic distribution of newly generated datasets to appropriate datacenters and exiting the spent datasets are still a challenge during workflows execution. To address this challenge, this study not only constructs a data placement model that includes shared datasets within individual and among multiple workflows across various geographical regions, but also proposes a data placement strategy (DYM-RL-DPS) based on algorithms of two stages. First, during the build-time stage of workflows, we use the discrete particle swarm optimization algorithm with differential evolution to pre-allocate initial datasets to proper datacenters. Then, we reformulate the dynamic datasets distribution problem as a Markov decision process and provide a reinforcement learning-based approach to learn the optimal strategy in the runtime stage of scientific workflows. Through simulating heterogeneous edge-cloud computing environments, we designed comprehensive experiments to demonstrate the superiority of DYM-RL-DPS. The results of our strategy can effectively reduce the data transmission time as compared to other strategies.